Comparative relation generative model

Online reviews are important decision aids to consumers. Other than helping users to evaluate individual products, reviews also support comparison shopping by comparing two (or more) products based on a specific aspect. However, making a comparison across two different reviews, written by different...

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Main Authors: TKACHENKO, Maksim, LAUW, Hady W.
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2017
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Online Access:https://ink.library.smu.edu.sg/sis_research/3754
https://ink.library.smu.edu.sg/context/sis_research/article/4756/viewcontent/ComparativeRelationGenerativeModel_TKDE_2017.pdf
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spelling sg-smu-ink.sis_research-47562019-06-04T01:48:49Z Comparative relation generative model TKACHENKO, Maksim LAUW, Hady W. Online reviews are important decision aids to consumers. Other than helping users to evaluate individual products, reviews also support comparison shopping by comparing two (or more) products based on a specific aspect. However, making a comparison across two different reviews, written by different authors, is not always equitable due to the different standards and preferences of authors. Therefore, we focus on comparative sentences, whereby two products are compared directly by a review author within a sentence. We study the problem of comparative relation mining. Given a set of comparative sentences, each relating a pair of entities, our objective is three-fold: to interpret the comparative direction in each sentence, to identify the aspect of each sentence, and to determine the relative merits of each entity with respect to that aspect. This requires mining comparative relations at two levels of resolution: at the sentence level, and at the entity level. Our insight is that there is a significant synergy between the two levels. We propose a generative model for comparative text, which jointly models comparative directions at the sentence level, and ranking at the entity level. This model is tested comprehensively on Amazon reviews dataset with good empirical outperformance over pipelined baselines. 2017-04-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/3754 info:doi/10.1109/TKDE.2016.2640281 https://ink.library.smu.edu.sg/context/sis_research/article/4756/viewcontent/ComparativeRelationGenerativeModel_TKDE_2017.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University comparative sentences Generative model comparison mining Databases and Information Systems Numerical Analysis and Scientific Computing
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic comparative sentences
Generative model
comparison mining
Databases and Information Systems
Numerical Analysis and Scientific Computing
spellingShingle comparative sentences
Generative model
comparison mining
Databases and Information Systems
Numerical Analysis and Scientific Computing
TKACHENKO, Maksim
LAUW, Hady W.
Comparative relation generative model
description Online reviews are important decision aids to consumers. Other than helping users to evaluate individual products, reviews also support comparison shopping by comparing two (or more) products based on a specific aspect. However, making a comparison across two different reviews, written by different authors, is not always equitable due to the different standards and preferences of authors. Therefore, we focus on comparative sentences, whereby two products are compared directly by a review author within a sentence. We study the problem of comparative relation mining. Given a set of comparative sentences, each relating a pair of entities, our objective is three-fold: to interpret the comparative direction in each sentence, to identify the aspect of each sentence, and to determine the relative merits of each entity with respect to that aspect. This requires mining comparative relations at two levels of resolution: at the sentence level, and at the entity level. Our insight is that there is a significant synergy between the two levels. We propose a generative model for comparative text, which jointly models comparative directions at the sentence level, and ranking at the entity level. This model is tested comprehensively on Amazon reviews dataset with good empirical outperformance over pipelined baselines.
format text
author TKACHENKO, Maksim
LAUW, Hady W.
author_facet TKACHENKO, Maksim
LAUW, Hady W.
author_sort TKACHENKO, Maksim
title Comparative relation generative model
title_short Comparative relation generative model
title_full Comparative relation generative model
title_fullStr Comparative relation generative model
title_full_unstemmed Comparative relation generative model
title_sort comparative relation generative model
publisher Institutional Knowledge at Singapore Management University
publishDate 2017
url https://ink.library.smu.edu.sg/sis_research/3754
https://ink.library.smu.edu.sg/context/sis_research/article/4756/viewcontent/ComparativeRelationGenerativeModel_TKDE_2017.pdf
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